Abstract: Background: Blockchain and distributed ledger technology is a disruptive force in healthcare. Methods: This article provides a globally relevant, interdisciplinary perspective intended to aid disparate group of actors, participants, and users that represent the diverse stakeholders of an increasingly complex and technologically reliant healthcare system. Domain expertise reinforced by literature published via industry, technical, and academic venues was used to inform these perspectives. Results: Key characteristics of blockchain and distributed ledger technology are highlighted and framed for a readership ranging from healthcare executive to policy makers to researchers. Antecedent application of blockchain in the financial sector is explored followed by the technical, security, and interoperability considerations specific to healthcare. Conclusion: Blockchain remains an emerging technology both fraught with unanticipated challenges and the promise of unrealized potential in healthcare. Keywords: Blockchain, Healthcare, Innovation, Adoption, Global, Interoperability

Abstract: Racial and ethnic disparities in cardiovascular disease (CVD) outcomes are widely reported, but research has largely focused on differences in quality of inpatient and urgent care to explain these disparate outcomes. The objective of this review is to synthesize recent evidence on racial and ethnic disparities in management of CVD in the ambulatory setting. Database searches yielded 550 articles of which 25 studies met the inclusion criteria. Reviewed studies were categorized into non-interventional studies examining the association between race and receipt of ambulatory CVD services with observational designs, and interventional studies evaluating specific clinical courses of action intended to ameliorate disparities. Based on the Donabedian framework, this review demonstrates that significant racial/ethnic disparities persist in process and outcome measures of quality of ambulatory CVD care. Multimodal interventions were most effective in reducing disparities in CVD outcomes.

Abstract: Purpose The US Food and Drug Administration's Sentinel system developed tools for sequential surveillance. Methods In patients with non-valvular atrial fibrillation, we sequentially compared outcomes for new users of rivaroxaban versus warfarin, employing propensity score matching and Cox regression. A total of 36 173 rivaroxaban and 79 520 warfarin initiators were variable-ratio matched within 2 monitoring periods. Results Statistically significant signals were observed for ischemic stroke (IS) (first period) and intracranial hemorrhage (ICH) (second period) favoring rivaroxaban, and gastrointestinal bleeding (GIB) (second period) favoring warfarin. In follow-up analyses using primary position diagnoses from inpatient encounters for increased definition specificity, the hazard ratios (HR) for rivaroxaban vs warfarin new users were 0.61 (0.47, 0.79) for IS, 1.47 (1.29, 1.67) for GIB, and 0.71 (0.50, 1.01) for ICH. For GIB, the HR varied by age: <66 HR = 0.88 (0.60, 1.30) and 66+ HR = 1.49 (1.30, 1.71). Conclusions This study demonstrates the capability of Sentinel to conduct prospective safety monitoring and raises no new concerns about rivaroxaban safety.

Abstract: Nowadays, a prosperity of electronic commerce (E-commerce) not only gives more convenience to consumers but brings more new opportunities in online advertising and marketing. Online advertisers can understand more about consumer preferences based on their daily online shopping and browsing behaviors. The development of big data and cloud computing techniques further empower advertisers and marketers to have a data-driven and consumer-specific preference recommendation based on the online browsing histories. In this research, a decision support system is proposed to predict a consumer purchase intention during browsing sessions. The proposed decision support system categorizes online browsing activities into purchase-oriented and general sessions using extreme boosting machines. With the browsing content entropy features, the proposed method achieves 41.81% recall and 34.35% F score. It further shows its strong predictive capability compared to other benchmark algorithms including logistic regression and traditional ensemble models. The proposed method can be implemented in real-time bidding algorithms for online advertising strategies. Ad deliveries on browsing session with potential purchase intention not only improve the effectiveness of advertisements but significantly increase last-touch attributions for campaign performance.

Abstract: Tuberous sclerosis complex (TSC) is a genetic disorder affecting multiple organ systems observed in approximately 1 in 10,000 live births.1,2 Patients with TSC have a higher risk of developing benign tumors in the brain, kidneys, heart, liver, lungs, and skin, and a variety of neuropsychiatric disorders.3 However, due to the rarity of TSC, its comorbidity landscape has not been fully investigated. To characterize the comorbidities of TSC systematically, we employed a nationwide health insurance claims dataset to analyze the comorbidity landscape of TSC and determined the phenotypical differences between patients with TSC and participants without TSC matched on age and sex.

Abstract: To explore the hypothesis that maternal periodontitis is associated with increased risk for Intrauterine Growth Restriction (IUGR), we examined the risk of IUGR in relation to periodontal treatment before, during and after pregnancy. We conducted a retrospective cohort analysis of insurance claims data from 2009 to 2012 for women who delivered a singleton live birth (n = 32,168). IUGR was examined as a function of type and timing of dental treatment, adjusting for potential confounders in logistic regression. Sensitivity analysis evaluated the potential effects of unmeasured confounding. Women who received periodontal treatment after delivery, indicating the presence of untreated periodontal disease during pregnancy, had significantly higher odds of IUGR compared to women who received no periodontal treatment (adjusted OR 1.5, 95% CI 1.2, 1.8). Periodontal treatment provided in the immediate postpartum period, a proxy for periodontitis during gestation, was associated with increased risk of IUGR.

Abstract: A method for assessing and responding to potential cybersecurity risks includes: obtaining, by a computing device, a plurality of attributes relating to an authentication event; determining, by the computing device, based on a cybersecurity risk assessment model, whether the plurality of attributes relating to the authentication event indicate a potential cybersecurity risk, wherein the cybersecurity risk assessment model is individualized on a per-user or per-device basis; and causing, by the computing device, in response to determining that the determined plurality of attributes relating to the authentication event indicate a potential cybersecurity risk, a heightened security measure to be implemented.

Abstract: A method, system and computer readable medium are provided for software defect reduction. To perform the software defect reduction implementation parameters for a software application in a development phase are collected, and an Extract, Transform and Load (ETL) is performed. The ETL analyzes data from one or more databases based on the implementation parameters to obtain relevant implementation data. The one or more databases store implementation data related to previously developed software applications, and the relevant implementation data is data stored in the one or more databases that is data that is relevant to the implementation parameters. The relevant implementation data is then summarized to obtain predicted data relevant to the software application in the development phase.

Abstract: Objective Schizophrenia (Sz) patients are among the highest utilizers of hospital-based services. Prevention of relapse is in part a treatment goal in order to reduce hospital admissions. However, predicting relapse is a challenge, particularly for payers and disease management firms with only access to claims data. Understandably, such organizations have had little success predicting relapse. A tool that allows payers to identify patients at elevated risk of relapse could facilitate targeted interventions prior to relapse and avoid rehospitalization. In this study, a series of proxy measures of patient instability, calculated from claims data were examined for their utility in identifying Sz patients at elevated risk of relapse. Methods Aetna claims were used to assess the relationship between instability of Sz patients and valence and magnitude of antipsychotic (AP) medication change during a 2-year period. Six proxies of instability including hospital admissions, emergency department visits, medication utilization patterns, and use of outpatient services were identified. Results were replicated using claims data from Truven MarketScan®. Results Patients who switched AP ingredient had the highest overall instability at the point of switch and the second steepest decline in instability following switch. Those who changed to a long-acting injectable AP showed the second highest level of instability and the steepest decrease in instability following the change. Patients augmented with a second AP showed the smallest increase in instability, up to the switch. Results were directionally consistent between the two data sets. Conclusion Using claims-based proxy measures to estimate instability may provide a viable method to better understand Sz patient markers of change in disease severity. Also, such proxies could be used to identify those individuals with the greatest need for treatment modification preventing relapse, improving patient outcomes, and reducing the burden of illness.

Abstract: Background: Evidence that physical activity can curb smoking urges is limited in scope to acute effects and largely reliant on retrospective self-reported measures. Mobile health technologies offer novel mechanisms for capturing real-time data of behaviors in the natural environment. Objective: This study aimed to explore this in a real-world longitudinal setting by leveraging mobile health tools to assess the association between objectively measured physical activity and concurrent smoking urges in a 12-week prospective observational study. Methods: We enrolled 60 active smokers (≥3 cigarettes per day) and recorded baseline demographics, physical activity, and smoking behaviors using a Web-based questionnaire. Step counts were measured continuously using the Fitbit Charge HR. Participants reported instantaneous smoking urges via text message using a Likert scale ranging from 1 to 9. On study completion, participants reported follow-up smoking behaviors in an online exit survey. Results: A total of 53 participants (aged 40 [SD 12] years, 57% [30/53] women, 49% [26/53] nonwhite) recorded at least 6 weeks of data and were thus included in the analysis. We recorded 15,365 urge messages throughout the study, with a mean of 290 (SD 62) messages per participant. Mean urge over the course of the study was positively associated with daily cigarette consumption at follow-up (Pearson r=.33; P=.02). No association existed between daily steps and mean daily urge (beta=−6.95×10−3 per 1000 steps; P=.30). Regression models of acute effects, however, did reveal modest inverse associations between steps within 30-, 60-, and 120-min time windows of a reported urge (beta=−.0191 per 100 steps, P<.001). Moreover, 6 individuals (approximately 10% of the study population) exhibited a stronger and consistent inverse association between steps and urge at both the day level (mean individualized beta=−.153 per 1000 steps) and 30-min level (mean individualized beta=−1.66 per 1000 steps). Conclusions: Although there was no association between objectively measured daily physical activity and concurrently self-reported smoking urges, there was a modest inverse relationship between recent step counts (30-120 min) and urge. Approximately 10% of the individuals appeared to have a stronger and consistent inverse association between physical activity and urge, a provocative finding warranting further study.

Abstract: A computing system for big data processing includes: a first node, configured to execute a central driver program; and a plurality of data and/or computing nodes, configured to store a plurality of data blocks corresponding to a data set. The first node and the plurality of data and/or computing nodes form a distributed computing environment configured for determining an exact value for one or more desired quantiles for the data set.

Abstract: Embodiments of the disclosure provide a method of incorporating multiple authentication systems and protocols. The types of authentication systems and protocols can vary based on desired assurance levels. A Centralized Authentication System together with an authentication policy dictates acceptable authentication systems. Authorization data for each authorization system are captured and packaged into a single Object Data Structure. The authorization data can be compared to data stored in an identity store for authentication. The authorization data can also be used for user and device registration and for transferring an authentication or registration token from a previously authenticated and registered device to a new device.

Abstract: Drug development for disease modifying agents in Alzheimer’s disease (AD) is focused increasingly on targeting underlying pathology in very early stages of AD or in cognitively normal patients at elevated risk of developing dementia due to Alzheimer’s. Very early interventional studies of this type have many uncertainties, including whether they can provide the clinical results that payers, providers, and patients will wish to see for decisions. This paper describes an initiative to create greater transparency for researchers to anticipate these decision needs. to create multi-stakeholder–vetted recommendations for the design of studies in later phases of drug development to evaluate the ability of disease modifying agents to delay or prevent the onset of dementia due to Alzheimer’s disease (AD). A multi-stakeholder expert workgroup and overseeing steering group were convened to discuss current advances in early interventional clinical trial design and the evidence needs of patients, providers, and payers. Eight teleconferences and one in-person all-day meeting were held. Meetings were recorded and summary notes prepared between sessions. Final conclusions were consolidated by the project team with the workgroup Chair based on these discussions and were reviewed by group members. The in-person meeting was held in Baltimore, MD In total, 36 stakeholders representing life sciences industry, payers or health technology assessors, patient advocates and research advocacy organizations, regulators, clinical experts and academic or NIH researchers. N/A. N/A. Certain aspects of clinical trial design were deemed important to address stakeholder decision needs for future Alzheimer’s prevention drugs even as the field rapidly progresses. These include the need for more robust behavioral and psychological outcome data in early symptomatic disease and the need to update activities of daily living measures to include “digital independence.” Amyloid, tau, and biomarkers of neurodegeneration should be included in trials and studied in relation to other early measures of change meaningful to individuals with AD, their families, and health plans. These measures include early sensitive changes in behavioral and psychological measures and ability to navigate the contemporary digital landscape. Additional work is needed to generate more robust behavioral and psychological outcome data in early symptomatic disease, and to generate multistakeholder consensus on early measures of change and magnitudes of change that will be meaningful to patients, providers, and payers.

Abstract: Embodiments of the disclosure provide a method of establishing a user profile using multiple channels. Embodiments allow compatibility of the user profile across several authentication systems. The user profile is created upon registration and is updated with attributes after authenticating and authorizing the user according to a pre-defined assurance level. The user profile contains attributes pertaining to the user and user device. The attributes can be analyzed by authentication systems to optimize data security.

Abstract: A method for secret sharing with required key(s) includes: generating, by a computing system, a secret key such that a minimum number of a plurality of shared keys, together with one or more required keys, are needed for derivation of the secret key; and encrypting, by the computing system, an element to be protected using the secret key.

Abstract: Disclosed is a method for minimizing computational resources when copying data. The method includes: receiving a first set of data from a first data source including portions (a) used to compute a well-being scoring, and (b) not used to compute the well-being scoring; copying non-numerical data items included in the portion used to compute the well-being scoring to an aggregate data structure; and, for each numerical data item in the portion used to compute the well-being scoring: assigning a first data type to the numerical data item if it complies with the first data type, otherwise assigning a second data type to the numerical data item, where the first data type uses less bytes than the second data type, and copying, by the processor, the numerical data item to the aggregate data structure, wherein the well-being scoring is calculated for the member based on the aggregate data structure.

Abstract: Even from the early days of business intelligence and analytics, there have been ample promises about what this type of solution can provide for decision-makers. Because of the new technologies that have been developed in recent years, business intelligence and analytics have once again garnered a lot of attention as more functionalities appear that finally seem to be delivering on the promise.

Abstract: In our first book together, written around 2015, we described how a complete, holistic BI solution involved three main classifications of reporting and analytics in general.

Abstract: Embodiments of the disclosure provide a medical identity theft prevention method performed by a computing server. The method includes: (a) registering an individual to an identity theft service, the registering comprising receiving individual identifying data from a computing device; (b) configuring a profile for the individual based on the individual identifying data; (c) monitoring use of a medical identity associated with the individual, the monitoring comprising receiving medical data from one or more provider devices; (d) determining from the medical data whether the medical identity is being misused; (e) in response to the determining that the medical identity being misused, alerting the individual through a victim device to the misuse of the medical identity; and (f) receiving a confirmation from the individual through the victim device, the confirmation indicating whether the medical identity is being used properly.

Abstract: Embodiments of the disclosure provide a method of providing a prompt to a user for reviewing an app. The method includes monitoring, by a mobile device, environmental factors; monitoring, b a mobile device and a server, a user's activity; determining, by the mobile device, a utility of the user using the environmental factors; determining, by the mobile device and the server, an engagement score using the user's activity; when the utility of the user is determined to be high, providing the prompt to the user; and when the utility of the user is determined to be low, determining whether the engagement score is above a score threshold, and providing the prompt to the user when the engagement score is above the score threshold.

Abstract: An embodiment of the disclosure provides a method of integrating data across multiple data stores in a smart cache in order to provide data to one or more recipient systems. The method includes automatically ingesting diverse data from multiple data sources, automatically reconciling the ingested diverse data by updating semantic models based on the ingested diverse data, storing the ingested diverse data based on one or more classification of the data sources according to the semantic models, automatically generating scalable service endpoints which are semantically consistent according to the classification of the data sources, and responding to a call from the one or more recipient systems by providing data in the classification of the data sources.

Abstract: BACKGROUND: Efforts at predicting long-term adherence to medications have been focused on patients filling typical month-long supplies of medication. However, prediction remains difficult for patie...